多传感器图像配准在空间图像处理中有非常重要的应用价值,但同时也面临着多源空间数据各异性困难。考虑到图像配准过程中的多分辨率视觉特征,采用基于小波的多分辨率图像分解来指导从粗到细的配准过程,利用扩展的轮廓跟踪算法提取满足视觉特征的轮廓,在轮廓链码曲率函数的基础上实现基于傅里叶变换的多分辨率形状特征匹配。与已有的基于特征的图像配准算法进行实验比较,实验结果表明该方法对于从多传感器得到的异质图像具有良好的配准效果。

Multi-sensor image registration is important for the spatial data processing, which especially faces the isomerism problems. In multi-modal image registration, the parameters of the transformation between the two images could be estimated by the corresponding pairs of control points. Facing with the difficulties of multi-modal images, this registration algorithm adopts the vision characters and multi-resolution based feature matching to carry out the new coarse-to-fine registration. It is composed of two steps. The first step is to set up wavelet-based image pyramid to decompose images into extract base contours and detail contours. In this step, an extended contour searching algorithm is developed to settle the image noises. The second step is to design a minimum distance classification based multi-resolution shape matching algorithm on the Fourier curvature representation of the chain code contour. Transformation parameters are estimated based on the final matched control-point pairs which are the centers of gravity of the closed contours. The multi-sensor Landsat TM imagery and infrared imagery have been used as experimental data to compare the algorithm with the classical contour-based registration. Experimental results show that this registration is superior to the classical ones.